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Spearman Correlation of Models
Summary of 1_Baseline
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Baseline Classifier (Baseline)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
1.0 seconds
Metric details
|
score |
threshold |
| logloss |
0.653366 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.526316 |
0.346154 |
| accuracy |
0.357143 |
0.346154 |
| precision |
0.357143 |
0.346154 |
| recall |
1 |
0.346154 |
| mcc |
0 |
0.346154 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.653366 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.526316 |
0.346154 |
| accuracy |
0.357143 |
0.346154 |
| precision |
0.357143 |
0.346154 |
| recall |
1 |
0.346154 |
| mcc |
0 |
0.346154 |
Confusion matrix (at threshold=0.346154)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
0 |
9 |
| Labeled as 1 |
0 |
5 |
Learning curves
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
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Summary of 2_DecisionTree
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Decision Tree
- n_jobs: -1
- criterion: gini
- max_depth: 3
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
4.7 seconds
Metric details
|
score |
threshold |
| logloss |
3.94729 |
nan |
| auc |
0.644444 |
nan |
| f1 |
0.5 |
0 |
| accuracy |
0.714286 |
0 |
| precision |
0.666667 |
0 |
| recall |
0.4 |
0 |
| mcc |
0.33735 |
0 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
3.94729 |
nan |
| auc |
0.644444 |
nan |
| f1 |
0.5 |
0 |
| accuracy |
0.714286 |
0 |
| precision |
0.666667 |
0 |
| recall |
0.4 |
0 |
| mcc |
0.33735 |
0 |
Confusion matrix (at threshold=0.0)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
8 |
1 |
| Labeled as 1 |
3 |
2 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
SHAP Importance
SHAP Dependence plots
Dependence (Fold 1)
SHAP Decision plots
Top-10 Worst decisions for class 0 (Fold 1)
Top-10 Best decisions for class 0 (Fold 1)
Top-10 Worst decisions for class 1 (Fold 1)
Top-10 Best decisions for class 1 (Fold 1)
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Summary of 3_Linear
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Logistic Regression (Linear)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
3.5 seconds
Metric details
|
score |
threshold |
| logloss |
1.45158 |
nan |
| auc |
0.466667 |
nan |
| f1 |
0.533333 |
0.0354484 |
| accuracy |
0.642857 |
0.867166 |
| precision |
0.5 |
0.867166 |
| recall |
1 |
0.00225237 |
| mcc |
0.141421 |
0.0354484 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
1.45158 |
nan |
| auc |
0.466667 |
nan |
| f1 |
0.285714 |
0.867166 |
| accuracy |
0.642857 |
0.867166 |
| precision |
0.5 |
0.867166 |
| recall |
0.2 |
0.867166 |
| mcc |
0.121716 |
0.867166 |
Confusion matrix (at threshold=0.867166)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
8 |
1 |
| Labeled as 1 |
4 |
1 |
Learning curves
Coefficients
| feature |
Learner_1 |
| Q23 |
0.976663 |
| Q7 |
0.74734 |
| Q33 |
0.742918 |
| Q10_2 |
0.732579 |
| Q15 |
0.637441 |
| Q40_1 |
0.535142 |
| Q30 |
0.502385 |
| Q32 |
0.444902 |
| Q25 |
0.389929 |
| Q3 |
0.340276 |
| Q22 |
0.29204 |
| Q10_1 |
0.276323 |
| Q6 |
0.183553 |
| Q9 |
0.16973 |
| Q1.1 |
0.142335 |
| Q36 |
0.114127 |
| Q40_2 |
0.111141 |
| Q28 |
0.0948538 |
| Q8 |
0.0494845 |
| Q20 |
0.029634 |
| Q19 |
0.0270623 |
| Q4 |
0.019808 |
| Q10_3 |
0.00353277 |
| Q18 |
-0.0227333 |
| Q34 |
-0.0323692 |
| Q11 |
-0.0677168 |
| Q35 |
-0.0753504 |
| Q1 |
-0.0867719 |
| Q27 |
-0.121918 |
| Q_40_3 |
-0.132504 |
| Q14 |
-0.13825 |
| Q5 |
-0.150052 |
| Q24 |
-0.153082 |
| Q21 |
-0.263028 |
| Q31 |
-0.269861 |
| Q16 |
-0.34809 |
| Q29 |
-0.479315 |
| Q26 |
-0.597044 |
| intercept |
-0.780512 |
| Q44 |
-1.25194 |
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
SHAP Importance
SHAP Dependence plots
Dependence (Fold 1)
SHAP Decision plots
Top-10 Worst decisions for class 0 (Fold 1)
Top-10 Best decisions for class 0 (Fold 1)
Top-10 Worst decisions for class 1 (Fold 1)
Top-10 Best decisions for class 1 (Fold 1)
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Summary of 4_Default_Xgboost
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Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: logloss
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
3.7 seconds
Metric details
|
score |
threshold |
| logloss |
0.515458 |
nan |
| auc |
0.844444 |
nan |
| f1 |
0.666667 |
0.0813726 |
| accuracy |
0.785714 |
0.177684 |
| precision |
1 |
0.402463 |
| recall |
1 |
0.0365983 |
| mcc |
0.547723 |
0.402463 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.515458 |
nan |
| auc |
0.844444 |
nan |
| f1 |
0.666667 |
0.177684 |
| accuracy |
0.785714 |
0.177684 |
| precision |
0.75 |
0.177684 |
| recall |
0.6 |
0.177684 |
| mcc |
0.518545 |
0.177684 |
Confusion matrix (at threshold=0.177684)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
8 |
1 |
| Labeled as 1 |
2 |
3 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
SHAP Importance
SHAP Dependence plots
Dependence (Fold 1)
SHAP Decision plots
Top-10 Worst decisions for class 0 (Fold 1)
Top-10 Best decisions for class 0 (Fold 1)
Top-10 Worst decisions for class 1 (Fold 1)
Top-10 Best decisions for class 1 (Fold 1)
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Summary of 5_Default_NeuralNetwork
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Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
1.3 seconds
Metric details
|
score |
threshold |
| logloss |
0.781839 |
nan |
| auc |
0.666667 |
nan |
| f1 |
0.625 |
0.0393083 |
| accuracy |
0.714286 |
0.698199 |
| precision |
1 |
0.698199 |
| recall |
1 |
0.0187985 |
| mcc |
0.389249 |
0.0393083 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.781839 |
nan |
| auc |
0.666667 |
nan |
| f1 |
0.333333 |
0.698199 |
| accuracy |
0.714286 |
0.698199 |
| precision |
1 |
0.698199 |
| recall |
0.2 |
0.698199 |
| mcc |
0.372104 |
0.698199 |
Confusion matrix (at threshold=0.698199)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
9 |
0 |
| Labeled as 1 |
4 |
1 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
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Summary of 6_Default_RandomForest
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Random Forest
- n_jobs: -1
- criterion: gini
- max_features: 0.9
- min_samples_split: 30
- max_depth: 4
- eval_metric_name: logloss
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
logloss
Training time
3.9 seconds
Metric details
|
score |
threshold |
| logloss |
0.651764 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.526316 |
0.323077 |
| accuracy |
0.357143 |
0.323077 |
| precision |
0.357143 |
0.323077 |
| recall |
1 |
0.323077 |
| mcc |
0 |
0.323077 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.651764 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.526316 |
0.323077 |
| accuracy |
0.357143 |
0.323077 |
| precision |
0.357143 |
0.323077 |
| recall |
1 |
0.323077 |
| mcc |
0 |
0.323077 |
Confusion matrix (at threshold=0.323077)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
0 |
9 |
| Labeled as 1 |
0 |
5 |
Learning curves
Permutation-based Importance
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
SHAP Importance
SHAP Dependence plots
Dependence (Fold 1)
SHAP Decision plots
Top-10 Worst decisions for class 0 (Fold 1)
Top-10 Best decisions for class 0 (Fold 1)
Top-10 Worst decisions for class 1 (Fold 1)
Top-10 Best decisions for class 1 (Fold 1)
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Summary of Ensemble
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Ensemble structure
| Model |
Weight |
| 1_Baseline |
1 |
| 4_Default_Xgboost |
5 |
Metric details
|
score |
threshold |
| logloss |
0.508274 |
nan |
| auc |
0.844444 |
nan |
| f1 |
0.666667 |
0.131913 |
| accuracy |
0.785714 |
0.212172 |
| precision |
1 |
0.399488 |
| recall |
1 |
0.0881909 |
| mcc |
0.547723 |
0.399488 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.508274 |
nan |
| auc |
0.844444 |
nan |
| f1 |
0.666667 |
0.212172 |
| accuracy |
0.785714 |
0.212172 |
| precision |
0.75 |
0.212172 |
| recall |
0.6 |
0.212172 |
| mcc |
0.518545 |
0.212172 |
Confusion matrix (at threshold=0.212172)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
8 |
1 |
| Labeled as 1 |
2 |
3 |
Learning curves
Confusion Matrix
Normalized Confusion Matrix
ROC Curve
Kolmogorov-Smirnov Statistic
Precision-Recall Curve
Calibration Curve
Cumulative Gains Curve
Lift Curve
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